7. Global Forest Watch & Monitoring Forests Using Remote Sensing

Global Forest Watch &Monitoring Forests Using

Remote Sensing

Dmitry AksenovTransparent World

1. Maps based on satellite data are a communication toolVisualizing a problem – a way for finding common language among stakeholders, helping them to understand each other.

2. Curtain lifting by independent satellite data– No restricted areas and no permission needed– No remote areas for satellites– Non-filtered information from direct physical measurement – nobody

could manipulate your interpretation– No one government , corporation or institution has a monopoly,

so attempts to classify satellite data fail– Satellite images – a “black box" of our planet: no way for hiding

something once recorded

3. Up-to-date and continuous information– Recent, often near-real-time information– True real-time technologies are coming– Time series available (basically for last 40 years)

Why satellite images importantfor forest monitoring?

GFW 1.0 (2000) – Mapping intact forest landscapes (IFL) – made a background for voluntary logging moratoriums

and new protected areas in different regions

Non-filtered data: the only (so far) post-soviet map of Russian forests published by Russian NGOs is based on satellite data

Photo: Ollivier Girard/CIFOR

Data Users

free

easy-to-use

interactive

timely

Forest Change Detection

frequent updates

high resolution

global coverage

Additional layers

• Additional forest change layers • Concessions (oil palm, logging, mining, etc)• Forest extent• Primary forest• Protected areas• Biodiversity hotspots• Forest carbon density• Community lands• Geo-tagged stories & photos

And more on the way……..

Global Forest Watch• Using maps and RS as a communication tool• Putting together data from different sources• Employing continuous monitoring tools• Allows user feedbackChallenges:• Good for a global view, needs adaptation on national

and local levels (WRI now working with UNEP to launch national projects for Georgia and Madagascar)

• Good in detected forest loss but weak in detecting forest degradation, often problematic with forest gain

• So far based on low and medium resolution RS data from open sources only

Low and medium resolution satellite data could be still very

useful for forest monitoring

Forest fires monitoring – among the most developed methods

Forest fires monitoring – among the most developed methods

Forest fires monitoring – among the most developed methods

Forest fires monitoring – among the most developed methods

Low and medium resolution data are good for areas with large-scale forest cover changes: clearcutting in Karelia, northwestern Russia

Deforestation in Central Kalimantandriven by oil palm plantations

Monitoring of deforestation (1) and palm oil plantations spreading (2) (Indonesia, Central Kalimantan)

Landsat 7 2001 Landsat 5 2006

2

2

1

1

Change of borders and dismemberment of forest(Madagascar, Zahamena Ankeniheny reserve)

Deforestation in MadagascarLandsat time series visualize changes

Landsat 205 June 1976

0 2.5 5 10km

Zahamena Ankeniheny reservefiery forest clearingnew non-forested areas

Landsat 505 June 1976Landsat 529 Sept 2001Landsat 521 Feb 2011

What could high- and very high-resolution satellite data add to the forest monitoring?• Selective / illegal logging monitoring

• Revealing reasons behind forest clearing

• Separating forests from plantations

• Tree species identification

• Pest outbreaks monitoring

• Identifying the most intact forest areas

• Assessing impact of forest fires

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Even for industrial selective logging in the Russian Far East

medium resolution is not enough.

Even for industrial selective logging in the Russian Far East medium resolution is not enough.

Example: logging outside of the permitted

Selective logging in question in European Russia, Moscow Region

Monitoring illegal logging in Laos

Selective loggingWorldView-2,0.5 meter /pixel

Revealing reasons behind forest clearing

Clearing for gold mining in Don Amphan NPA, Laos

Worldview-2, 21st December 2012Resolution: 0.5 m

©DigitalGlobe Inc. 2012distributed by R&DC ScanEX

Gold Mining in Don Amphan NPA, Laos

©DigitalGlobe Inc. 2012 Distributed by R&DC Scanex ©Transparent World

WorldView-221st December, 2012Resolution: 0.5 m

Separating forests from plantations

Plantation rotation cycleSumatra, Indonesia

Separating forests from plantations in tropics,identifying types of plantations

A – oil palmB, C – non-palmD – Secondary forest / abandoned plantation

Automatic algorithms for single tree mapping

Moscow region, GeoEye images, August 2012Measuring forest damage of pest outbreak

STEP1: Segmentation of spectral channel (resolution-10m; min. area 50 pix.)

Classifying forests by degradation level in Madagascar:separating natural multilayer forests from secondary and degraded

STEP 2: Calculate local reflections minimum points frompanchromathic channel (resolution-2.5m;window 5*5 pix)

Classifying forests by degradation level in Madagascar:separating natural multilayer forests from secondary and degraded

STEP 3: Select “gaps” between trees of different size local minimum points with reflection less 80 DN

Classifying forests by degradation level in Madagascar:separating natural multilayer forests from secondary and degraded

STEP 4: Calculate density of “gaps” (count points inside polygons/areaOf each polygons*100) on 100 sq.m.

Classifying forests by degradation level in Madagascar:separating natural multilayer forests from secondary and degraded

STEP 5: Maps of forest structure “compexity” based on density of “gaps”

Classifying forests by degradation level in Madagascar:separating natural multilayer forests from secondary and degraded

STEP7: Classification

Classifying forests by degradation level in Madagascar:separating natural multilayer forests from secondary and degraded

However, high resolution imagery is still pretty expensive. There is

always a balancing between price and quality

Solution 1: Weighting price against resolution & spectral channels

• 1.5-2.0 m. resolution data vs. 0.5-1.0 m.(Airbus vs. DG ?)

• Panchromatic (b&w) images vs. multi-spectral (color) images

• Larger scene size

Solution 2: Supporting sharing the satellite data

• International institutions and governments should buy licenses for multiple users (usually for little extra funding)

• Influencing satellite operators for shared license policy (one acquired the image could be shared)

• Contributing information into the public domain, at least for non-profit applications

Solution 3: Supporting open satellite data

• Supporting continuation of Landsat missions, Sentinel mission

• Supporting image donation programs of private operators

• Supporting open data policies from the governments

Open data web portal of the Russian Space Agency

Canopus-B coverage for Georgia

Resource-DK1 coverage for Georgia

Tbilisi suburbs in the portal (Resource-DK1)

Open Landscape Partnership Platform: involve more people in using high resolution data for public sector monitoring projects around the world

Open Landscape Partnership Platform: involve more people in using high resolution data

for public sector monitoring projects around the world

Donate free access to VHR satellite data, provide simple tools to access and process them online

Engage local government, land management agencies, project entities, and civil society organizations

Invite a number of crowd-mapping projects in various countries

Strengthen social and environmental accountability in and around significant conservation landscapes and hotspots

Possible sources of the high-resolution images for GFW for Georgia

• Russian high-resolution satellites• Possible donation of Israeli EROS-B satellite (0.7

meters per pixel, panhromatic)• RapidEye data already acquired by GIZ• Old WB-paid air photos• Possible donation from Airbus (SPOT data) – tbd

• After all, Georgia is a small country. Why not to buy some data (WRI, WB, FLEG)?..

Solution 4: Reducing prices for VHR data as a market for non-military application would grow-up

More projects involving VHR data

VHR: too expensive for public sector

Demand expands as the benefits are demonstrated

New mechanisms are developed for sustaining the

supply to public sector

Raising the interest of satellite operators for public sector

applications

Limited market for public sector applications

Insufficient frequency and coverage for public sector

applications

Not a priority for satellite operators

Solution 4: Scaling up

Data acquisition

HIGHER POSSIBLE PRICESHIGH TOTAL EXPENSES, CHEAPER PRICES PER SCENE

LOWEST PRICE PER SCENE

Ground receiving stations

Long-term contracts with

satellite operators

Single scenes purchasing

Monitoring for a single province in Niger may be expensive comparing to the price of a tree planting project.

The monitoring price for the whole Sahel area would be insufficient in the overall budget.

The generation of compact ground station is already at place

License agreements

License agreements on operational data reception:2001 November IRS-1C/1D2004 October RADARSAT-12005 February Monitor-E2005 April EROS A2005 October IRS-P62006 March SPOT 42006 August EROS B 2006 September IRS-P52007 April ENVISAT-12009 June CARTOSAT-22009 July SPOT-52009 July Formosat-2 2011 July RADARSAT-22011 December UK-DMC2

2007 June IKONOS2007 March TerraSAR-X2007 December ALOS 2008 May Kompsat-2 2009 January GeoEye 2012 May QuickBird2012 May WorldView-1 2012 May WorldView-2

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Experience available already

Nizhny-Novgorod State University after LobachevskyNizhny-Novgorod State University of Architecture & Civil Engineering after BaumanUfa State Aviation Technical UniversityTyumen State University Astrakhan State UniversityAltai State UniversityTomsk State University of Control Systems and RadioelectronicsSt-Petersburg State University of Aerospace InstrumentationMoscow State University of Geodesy and Cartography

Moscow State University St-Petersburg State University

Southern Federal UniversitySiberian Federal UniversityUral Federal UniversityNorthern (Arctic) Federal UniversityNorth-Caucasus Federal University

University of Valencia, SpainUniversity of Valladolid, Spain

Kazakh-British Technical University, AlmatyKazakhstan National Technical University after Satpaev

27 RS centers at universities in Russia, Kazakhstan and Spain

Belgorod State UniversityNational Mineral Resources University, St-PetersburgSaratov State University after ChernychevskyPerm State UniversitySiberian State Aerospace University after ReshetnikovSamara State Aerospace University after Korolev

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Scanex receiving station installations in the universities

University competence centers• Equipped with ground stations

• Having access to multiple satellites

• Using image processing software complementing ground stations

• Opening access to satellite data and products through university web portals (or/and shared portal / library)

Coming soon: small-size satellites

• SPUTNIX – a startup daughter company by Scanex

• A platform for low-orbiting small-size satellites of 10.. 50 kg

• 20-25 meters / pixel resolution in up to four spectral channels

• Up to 15 meters / pixel resolution in panchromatic

• About 20 days turnover

• 45.. 500 km wide scenes

• Successfully launched in June 2014

Thank you!

Dmitry AksenovTransparent World

picea2k@gmail.com